Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
Gene-Environment Interactions01:20

Gene-Environment Interactions

Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
Pedigree Analysis01:35

Pedigree Analysis

Overview
Pedigree Analysis01:35

Pedigree Analysis

Overview
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Evaluating the potential of acupuncture for Alzheimer's disease treatment: A meta-analysis and systematic review of mouse model studies.

Translational psychiatry·2026
Same author

Navigating Cognitive Maps: Statistical Analysis of 3D Path Data in Minecraft.

Psychometrika·2026
Same author

Amelogenin Peptide Promotes Human Dental Pulp Cell Proliferation and Odontogenic Differentiation via ERK1/2 Pathway.

International dental journal·2025
Same author

Nationwide expert survey on transfusion and coagulation management strategies for bleeding critically ill patients in China.

Journal of critical care·2025
Same author

TIME-VARYING <i>ℓ</i> <sub>0</sub> OPTIMIZATION FOR SPIKE INFERENCE FROM MULTI-TRIAL CALCIUM RECORDINGS.

Data science in science·2025
Same author

USP7 Stabilizes USF1 to Aggravate ox-LDL-Induced Endothelial Injury Through the MYD88/NF-κB Pathway in Atherosclerosis.

Applied biochemistry and biotechnology·2025

Related Experiment Video

Updated: May 30, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Testing gene-gene interactions in the case-parents design.

Zhaoxia Yu1

  • 1Department of Statistics, University of California, Irvine, CA 92697, USA. zhaoxia@ics.uci.edu

Human Heredity
|July 23, 2011
PubMed
Summary

Conditional logistic regression tests for gene-gene interactions are robust when main genetic effects are fully flexible. This approach prevents spurious associations and maintains accurate error rates, making it ideal for genetic association studies.

More Related Videos

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Related Experiment Videos

Last Updated: May 30, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Area of Science:

  • Genetics
  • Statistical Genetics
  • Epidemiology

Background:

  • Case-parents designs are effective for detecting genetic associations and preventing spurious findings common in population-based studies.
  • Conditional logistic regression, by conditioning on parental genotypes, offers complete protection against population stratification when analyzing single genetic loci.
  • The robustness of conditional logistic regression for testing gene-gene interactions remains an open question.

Purpose of the Study:

  • To evaluate the robustness and efficiency of gene-gene interaction tests based on conditional logistic regression.
  • To identify reliable methods for detecting gene-gene interactions in the presence of genetic correlations.

Main Methods:

  • Evaluation of several gene-gene interaction tests derived from conditional logistic regressions.
  • Assessment of test performance under conditions of SNP genotype correlation (population stratification, linkage disequilibrium).
  • Comparison of tests with different specifications for main genetic effects.

Main Results:

  • Tests with incorrectly specified main genetic effects exhibit inflated Type I error rates when SNP genotype correlation is present.
  • A test incorporating fully flexible main genetic effects consistently maintains correct test size (Type I error rate).
  • The fully flexible main effects test achieves robustness with minimal loss of statistical power.

Conclusions:

  • For gene-gene interaction testing, utilizing conditional logistic regression with fully flexible main genetic effects is recommended.
  • This approach ensures accurate results by mitigating the impact of population stratification and linkage disequilibrium.
  • The recommended method provides a robust and powerful tool for genetic association studies focusing on interactions.